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  • Cao, Yinghao  (2)
  • 1
    Online Resource
    Online Resource
    Development and Interpretation of a Clinicopathological-Based Model for the Identification of Microsatellite Instability in Colorectal Cancer
    Hindawi Limited ; 2023
    In:  Disease Markers Vol. 2023 ( 2023-2-18), p. 1-12
    Details
    In: Disease Markers, Hindawi Limited, Vol. 2023 ( 2023-2-18), p. 1-12
    Abstract: Chemotherapy is not recommended for patients with deficient mismatch repair (dMMR) in colorectal cancer (CRC); therefore, assessing the status of MMR is crucial for the selection of subsequent treatment. This study is aimed at building predictive models to accurately and rapidly identify dMMR. A retrospective analysis was performed at Wuhan Union Hospital between May 2017 and December 2019 based on the clinicopathological data of patients with CRC. The variables were subjected to collinearity, least absolute shrinkage and selection operator (LASSO) regression, and random forest (RF) feature screening analyses. Four sets of machine learning models (extreme gradient boosting (XGBoost), support vector machine (SVM), naive Bayes (NB), and RF) and a conventional logistic regression (LR) model were built for model training and testing. Receiver operating characteristic (ROC) curves were plotted to evaluate the predictive performance of the developed models. In total, 2279 patients were included in the study and were randomly divided into either the training or test group. Twelve clinicopathological features were incorporated into the development of the predictive models. The area under curve (AUC) values of the five predictive models were 0.8055 for XGBoost, 0.8174 for SVM, 0.7424 for NB, 8584 for RF, and 0.7835 for LR (Delong test, P value 〈 0.05). The results showed that the RF model exhibited the best recognition ability and outperformed the conventional LR method in identifying dMMR and proficient MMR (pMMR). Our predictive models based on routine clinicopathological data can significantly improve the diagnostic performance of dMMR and pMMR. The four machine learning models outperformed the conventional LR model.
    Type of Medium: Online Resource
    ISSN: 1875-8630 , 0278-0240
    URL: Article
    DOI: 10.1155/2023/5178750
    Language: English
    Publisher: Hindawi Limited
    Publication Date: 2023
    detail.hit.zdb_id: 2033253-1
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  • 2
    Online Resource
    Online Resource
    Image5.TIF
    Frontiers Media SA ; 2022
    In:  Frontiers in Genetics Vol. 13 ( 2022-4-4)
    Details
    In: Frontiers in Genetics, Frontiers Media SA, Vol. 13 ( 2022-4-4)
    Abstract: RimK-like family member B (RIMKLB) is an enzyme that post-translationally modulates ribosomal protein S6, which can affect the development of immune cells. Some studies have suggested its role in tumor progression. However, the relationships among RIMKLB expression, survival outcomes, and tumor-infiltrating immune cells (TIICs) in colorectal cancer (CRC) are still unknown. Therefore, we analyzed RIMKLB expression levels in CRC and normal tissues and investigated the correlations between RIMKLB and TIICs as well as the impact of RIMKLB expression on clinical prognosis in CRC using multiple databases, including the Tumor Immune Estimation Resource (TIMER), Gene Expression Profiling Interactive Analysis (GEPIA), PrognoScan, and UALCAN databases. Enrichment analysis was conducted with the cluster Profiler package in R software to explore the RIMKLB-related biological processes involved in CRC. The RIMKLB expression was significantly decreased in CRC compared to normal tissues, and correlated with histology, stage, lymphatic metastasis, and tumor status ( p & lt; 0.05). Patients with CRC with high expression of RIMKLB showed poorer overall survival (OS) (HR = 2.5, p = 0.00,042), and inferior disease-free survival (DFS) (HR = 1.9, p = 0.19) than those with low expression of RIMKLB. TIMER analysis indicated that RIMKLB transcription was closely related with several TIICs, including CD4 + and CD8 + T cells, B cells, tumor-associated macrophages (TAMs), monocytes, neutrophils, natural killer cells, dendritic cells, and subsets of T cells. Moreover, the expression of RIMKLB showed significant positive correlations with infiltrating levels of PD1 (r = 0.223, p = 1.31e-06; r = 0.249, p = 1.25e-03), PDL1 (r = 0.223, p = 6.03e-07; r = 0.41, p = 5.45e-08), and CTLA4 (r = 0.325, p = 9.68e-13; r = 0.41, p = 5.45e-08) in colon and rectum cancer, respectively. Enrichment analysis showed that the RIMKLB expression was positively related to extracellular matrix and immune inflammation-related pathways. In conclusion, RIMKLB expression is associated with survival outcomes and TIICs levels in patients with CRC, and therefore, might be a potential novel prognostic biomarker that reflects the immune infiltration status.
    Type of Medium: Online Resource
    ISSN: 1664-8021
    URL: Article
    URL: Article
    URL: Article
    URL: Article
    URL: Article
    URL: Article
    DOI: 10.3389/fgene.2022.818994
    DOI: 10.3389/fgene.2022.818994.s001
    DOI: 10.3389/fgene.2022.818994.s002
    DOI: 10.3389/fgene.2022.818994.s003
    DOI: 10.3389/fgene.2022.818994.s004
    DOI: 10.3389/fgene.2022.818994.s005
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2022
    detail.hit.zdb_id: 2606823-0
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